Week 2

 In week 2, we finally get the equipment(Raspberry PI, LCD screen. camera, SD card) for our project, but we encountered multiple challenges, including SD card burning failures and difficulties accessing the Raspberry Pi system desktop. At the same time, we began transitioning from traditional computer vision methods to deep learning for improved license plate recognition accuracy.


Lab Day

Using Raspberry PI pin map and LCD pin layout as guide, we built the circuit.


Challenges and Issues Faced

SD Card Burning Failure

One of the major obstacles was the failure to burn the operating system onto the SD card. Several attempts were made using different SD cards and flashing software such as Balena Etcher and Raspberry Pi Imager. The common issues encountered included:

  • SD card not being recognized by the system.

  • Flashing process getting stuck midway.

  • Errors upon booting the Raspberry Pi after flashing.

To address these issues, we tested multiple SD cards, reformatted them using different file systems (FAT32, exFAT), and ensured the power supply was stable during the flashing process.


Accessing the Raspberry Pi Desktop

After successfully flashing the OS, we faced another challenge: the Raspberry Pi system did not boot properly into the desktop environment. Issues included:

  • The screen remaining blank after startup.

  • Unexpected system crashes when launching the desktop interface.

  • HDMI display compatibility issues.


Next Steps

  • Complete the setup of the Raspberry Pi environment for model deployment.

  • Train and fine-tune deep learning models for real-time license plate recognition.








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